Single-sensor acoustic emission source localization in blade local region based on an interpretable wavelet attention network

Acoustic emission (AE) technology is suitable for monitoring wind turbine blades because of its high-frequency response and sensitivity. However, existing damage localization approaches rely on multi-sensor arrays, which limit their engineering applicability. Moreover, deep learning models for single-sensor localization remain constrained by insufficient time-frequency representation and limited interpretability. To address these issues, a novel single-sensor AE source localization model, the Large-kernel dual wavelet attention network, was proposed. First, pencil lead break tests on real blade structures were conducted to investigate AE signal propagation, revealing strong nonlinear behavior and multi-scale attenuation characteristics. On this basis, a Large-kernel dual wavelet convolution layer was developed using Morlet and Mexican hat wavelets. With a mechanism-guided large wavelet kernel design, the arbitrariness in hyperparameter selection and the Gibbs phenomenon associated with incomplete truncation were mitigated, enabling the effective extraction of multi-scale time-frequency features. A time-grouped frequency attention mechanism module was introduced to adaptively recalibrate convolutional kernel weights through the fine-grained perception of localized time-frequency features, thereby improving the discrimination of critical features. Experiments on a real blade specimen demonstrated that the proposed method outperformed existing models in localization accuracy, achieving a minimum mean distance error of 20.29 mm. Furthermore, the model exhibited advantages in terms of feature interpretability and noise robustness. These results provide a promising and interpretable framework, laying the foundation for single-sensor damage localization in structural health monitoring of wind turbine blades.